History suggests that safety mechanisms designed for a given level of capability do not necessarily work for systems that are much more capable. For example, financial rules designed for days when humans did trading in a trading pit had to be modified with the advent of electronic trading. Similarly, in aviation, when air traffic increased from the levels in the early “barnstorming” era, new regulations and systems were required to ensure air safety.

The question for AI is whether the same pattern applies – and whether it is possible to design a system where safety improves at the same speed that capability grows rather than lagging behind it.

Such a design is possible.

But for it to be successful, the design must follow a key rule: every improvement must maintain or increase human safety. A system change that reduces ethical oversight typically would not qualify. Nor would an upgrade that concentrates control in fewer hands, or that allows safety constraints to be relaxed under competitive pressure.

To succeed, the rule should be built into the architecture itself, not tacked on as a policy that might be changed later.

Here is an example of how this rule is embedded in our architecture:

• Customization. When an Agent’s owner corrects an ethical error, that correction improves the individual AI agent’s judgment. Through integration, the correction also flows into the collective model, refining the ethical standards that govern AGI behavior across the network. Every correction made by any owner anywhere on the network makes the system slightly more ethically calibrated.

• Architecture. The problem-solving framework can recalibrate through experience. If certain safety checks prove too restrictive in some domains, they can be relaxed provided they do not decrease overall safety. If others prove too permissive, they can be tightened. The safety mechanisms are dynamic and self-optimizing.

• Network. The reputation system learns to identify problematic participants more accurately over time. When bad actors find new ways to abuse the system, patterns are recognized, and defenses are updated. Safety mechanisms learn to become more accurate and effective at detecting new types of threats as they emerge.

• Integration. Voting and democratic participation enable the contributor community to address new ethical issues as they arise. The ethical foundation evolves with human understanding and with changes in human ethics over time.

Another important principle is to avoid unrecoverable outcomes.

Decisions that cannot be reversed if they prove mistaken should be treated as categorically different from decisions that can be corrected. Our architecture prioritizes avoiding catastrophic scenarios over maximizing short-term capability.

Competitive pressure often tempts companies to move faster, deploy sooner, and address safety concerns later.

Our improvement architecture removes that temptation structurally. Capability and safety improve through the same mechanisms. You cannot accelerate one without accelerating the other. For example, as more agents are added, the system’s values become MORE representative of the human population. As more problems are solved, the system learns what it means to solve problems in ethical ways approved by humans. As the system’s speed and capability increase, the frequency and speed of ethical checks also increase, as they are built into the thinking cycle used to solve each problem.

Whether our architecture is adopted or not, advanced AI must be developed so that safety is built into the architecture.

As the system’s capability increases, the safety checks MUST increase automatically and proportionally to maintain or increase the overall safety level. This feature should be considered a hard design constraint for every developer working on advanced AI systems.

In the next post, we step back from the subsystems and ask what the world looks like when this architecture is operating at scale.

For AI researchers who want details of the approach, we recommend starting with White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see how it all works.

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In most current approaches, a small team of researchers and ethicists makes that decision for everyone. These are often thoughtful, well-intentioned people. But should ANY small group be trusted to determine the ethical foundations of a technology that will eventually affect 8.3 billion humans?

Our architecture takes a different approach.

It aggregates the ethical values of millions of individual contributors into a collective model through a transparent, auditable process.

Here is how it works.

During the customization process described in Post 4 (Your AI Should Think Like You), ethical knowledge is captured in the same way as domain knowledge. When an AI agent’s owner corrects his AI agent’s outputs, specifies his preference for Fair Trade cafes, or answers questions about his values, those judgments become part of his agent’s ethical profile alongside his factual knowledge. Every customized AI agent on the network carries its owner’s ethical values as a built-in part of its training.

When the Integration subsystem aggregates individual agent datasets into a collective model, it aggregates ethical information alongside factual and procedural knowledge.

This happens through three methods:

1. Dataset aggregation. All ethical data from millions of humans is combined into a collective corpus, and the integrated system is trained on it. The majority views across millions of contributors naturally carry more weight than minority views, creating a form of implicit democratic representation without any single authority deciding whose values count more.

2. Weighted averaging. Individual ethical contributions can be assigned explicit weights based on documented criteria. Contributors with a demonstrated ethical track record across many network interactions might (optionally) carry more weight on contested ethical questions in their domains of expertise. The weighting methodology is documented and transparent, allowing the community to audit it and set the rules.

3. Machine learning-based aggregation. More sophisticated methods identify consensus positions across contributors, detect outliers, and resolve apparent contradictions in ways that reflect deeper patterns in the ethical training data that can supplement the majority positions.

Voting extends this democratic participation further. Users can vote on specific ethical questions that affect how the AGI behaves. For example, humans can vote on which philosophical frameworks should carry more weight in contested situations, how the system should behave when competing values conflict, and what categories of decisions should always require human authorization. These mechanisms help the community shape the AGI system’s values.

There is a philosophical point here that matters. The philosopher, David Hume, explained as early as 1776 that there is no way to derive values logically from first principles. More recently, this point was amplified by the Nobel Laureate and AI pioneer Herbert A. Simon, in his book Reason in Human Affairs.

Even an intelligence far more capable than any human cannot reason its way to values. It must get them from some source other than logical reasoning. Values that emerge from a diverse, democratic process have a legitimacy that values imposed by any small group cannot.

The safeguard against ethical going wrong is transparency.

The methodology for aggregating ethics must be documented so contributors can understand how their input affects the AGI’s collective values. When the aggregated ethics produce problematic results, the methodology can be examined and corrected. Accountability must be built into the architecture, not added as an afterthought.

Properly designed, the result is an AGI whose ethics are not simply programmed in. They grow from millions of real human contributions, are refined through interaction, and are continuously updated as the contributor base evolves. This is one of the few approaches to AI alignment that does not require trusting that the people in charge of training are well-intentioned and also able to determine the right values for everyone.

In the next post, we look at the Improvement subsystem, and the key constraint that governs every upgrade the system makes.

For AI researchers who want details of the approach, we recommend starting with White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see how it all works.

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Collaboration is one thing. Synthesis is another. The four components described so far (Customization, Architecture, Network, and Trust) all feed into the fifth: Integration. This is where individual capabilities generalize. This is where the system achieves the ability to solve any problem as well as the average human, a competence level called Artificial General Intelligence or AGI.

Pooling millions of individual knowledge sources does not automatically produce something more capable than its parts. Raw aggregation produces noise as readily as it produces insight. Integration, rather than mere aggregation, is needed to create an AGI that improves, and keeps improving, as more people participate.

Integration involves two complementary mechanisms.

The first is data aggregation. Most customized AI agents will contain at least some unique data. Pooling the data allows us to train new Agents on the combined corpus. These new Agents would have access to everything any individual agent has ever learned, a breadth of experience no individual human could achieve.

But our system goes further. Individual agent contributions can be weighted based on their value to the collective. The White Paper describes three methods for measuring that value:

• We can train models with and without specific contributions to measure the additional improvement each one provides.

• We can repeatedly sample from available data to test whether a contribution’s benefits are consistent or accidental.

• We can assess how well knowledge from one domain improves performance in (or transfer to) another, capturing the contributions that generalize most broadly.

Data that consistently improves collective capability receives greater weight. Data that merely adds redundancy receives less. Measuring the value of contributed information can also help determine how agents (or their owners) should be compensated for adding their data. Contributors whose AI agents yield genuinely novel knowledge earn more than those whose knowledge duplicates what the system already has. The system should not pay for AI-generated slop, or information that is already well known.

The second mechanism is procedural integration. When an AI agent successfully solves a problem, the solution path can be encoded as a reusable procedure and made available across the entire network. The next time the AGI system encounters the same or a similar problem, instead of having to generate the solution from scratch, it can simply retrieve it, since the problem has already been solved. This allows the overall AGI system to learn and improve in much the same way that humans do.

White Paper 1, Advanced Autonomous Artificial Intelligence Systems and Methods, describes a royalty mechanism that incentivizes contributors to help the AGI learn. When a contributor’s prior solution gets incorporated into a new solution by a different solver on a future problem, the original contributor can automatically earn a royalty. Creating a high-quality, broadly applicable solution is financially valuable long after the original problem is closed. This creates an incentive to develop modular, well-documented approaches rather than one-off fixes. Over time, the repository of reusable solutions grows, and the network’s collective problem-solving capability compounds as valuable procedures accumulate. Note that some of the token cost saved by re-using learned solutions, instead of having to generate them from scratch, can be used to pay royalties, so that the royalties are self-funding.

Procedural integration also captures failure knowledge. Dead ends, abandoned approaches, and the hard-won understanding of what not to try are all recorded in the problem tree. That record is valuable because it can help the AGI avoid dead ends when confronted with a novel problem.

The result is a system that learns at two levels simultaneously. Individual agents improve through their own experience and continued customization. The collective model improves through the aggregated contributions of all agents on the network.

One design feature matters particularly for safety: the integration process is auditable. The contributions that shaped the collective model can be traced. The assessment methods and weighting decisions can be examined. The proceduralized solutions that the AGI learns can be examined critically. The ethical inputs can be reviewed. An AGI whose development process is documented and reviewable can be corrected if something goes wrong. An AGI whose development process is opaque cannot. Auditability is a prerequisite for accountability.

In the next post, we look at the most consequential dimension of integration: how the ethical values of millions of human contributors become the ethical foundation of AGI itself, and why the governance of that process matters more than almost any other design decision.

For AI researchers who want details of the approach, we recommend starting with White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see how it all works: superintelligence.com/whitepaper1-aaai-systems-methods.

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Essentially, this is an issue of trust. As in human relationships, AI agents must earn trust over time, through their actions. A well-designed architecture enables this principle. For example, in the AAAI architecture, every AI agent on the network operates at one of three supervision levels. Owners can adjust that level at any time.

Supervised.

The owner reviews most of the AI agent’s actions. Problem acceptance, solution development, and final deliverables all require owner attention before moving forward. This is appropriate for a new AI agent in unfamiliar domains or situations where errors would be costly. The agent has no track record yet. The owner has no basis for confidence. Tight oversight at this stage is how both the agent and the owner learn what the agent is capable of.

Semi-autonomous.

The AI agent handles most work independently but flags specific decisions for owner review. High-stakes choices, ethically sensitive situations, and problems at the edge of the AI agent’s competence still come back to the owner. Everything else proceeds without interruption. This is the level where most established AI agents operate, balancing efficiency with the human judgment that matters most.

Autonomous.

The AI agent accepts problems, develops solutions, and delivers results without requiring owner involvement. This is appropriate only for routine problems within the agent’s established areas of competence, where the risk of error is low, and the consequences of a mistake are manageable. Even at this level, anything that could cause serious or irreversible harm remains outside the scope of autonomous operation, regardless of the agent’s track record.

Supervision level is not fixed.

Owners can tighten control when problems arise and relax it as confidence builds. The system provides dashboards, notifications, and logging to make this practical. An owner who notices a pattern of errors can move an AI agent back to supervised mode without penalty.

There is a financial dimension that gives this structure teeth. Autonomous AI agents can handle higher workloads and earn more income. Supervised agents earn less because the requirement for owner supervision limits their throughput. This creates a natural incentive to earn autonomy responsibly. Rushing to autonomous operation before the track record justifies it leads to errors that damage reputation, reduce access to high-value problems, and reduce income: the economics and the safety properties are aligned.

Voting and staking mechanisms add a community layer of accountability on top of individual owner oversight. When participants vote on whether a proposed action or solution is ethically acceptable, they can stake real value on their judgments. Correct assessments earn rewards. Incorrect ones carry penalties. Every active participant on the network has a personal financial stake in ensuring highly ethical decision-making.

The goal is not permanent human control of every decision. That does not scale. Human bottlenecks, oversight fatigue, and the tendency to approve decisions without scrutiny when volume gets too high are problems for AI governance. Our architecture has prioritization methods to keep humans meaningfully involved in the decisions that matter most, while giving AI agents the freedom to operate where their track record has already established their reliability.

In the next post, we examine how to integrate individual contributions from millions of agents into SuperIntelligent performance.

For AI researchers who want details of the approach, we recommend starting with White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see how it all works.

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A fundamental problem with AI safety rules is that any rule that can be written into a system can also be written out, or hacked. This problem implies that safety must largely align with self-interest if we expect the rules to remain operational.

For example, in the cybersecurity arena, when an LLM can find hacks faster than cybersecurity experts can prevent them, we have a serious problem. The root cause is not technology but the fact that hackers’ self-interest is not aligned with the safety concerns of the larger community. There are many technical solutions to prevent cybercrime, just as there are many law enforcement techniques to prevent other forms of criminal behavior. Yet the way AI systems are designed and the incentives inherent in those designs can have a huge effect on whether they encourage or discourage criminal behavior.

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Our designs create an economic environment in which good behavior is the most profitable strategy available and bad behavior is self-defeating. Incentives for prosocial behavior follow naturally from the system’s design.

Here is how it works.

People with customized AAAIs (aka AI agents) deploy them in a marketplace to solve clients’ problems. The marketplace identifies which AAAIs have the expertise a client needs, factors in the AI agent’s reputation, availability, and price, and connects the AI agent to the client’s problem accordingly. When a problem is too complex for any single AAAI, the network decomposes it into sub-problems and recruits specialist AI agents for each sub-problem. This is one way the network can achieve SuperIntelligent performance from individual AI agents that are not SuperIntelligent on their own.

Before any AI agent can participate, the platform sets baseline standards that all AAAIs must meet. This is the first layer of safety: a floor below which no participant can operate, regardless of reputation. Reputation then builds on top of that floor.

The village water system example from White Paper 1, introduced in an earlier post, illustrates how matching agents with sub-problems works.

• A development organization submits a single problem: design a plan for bringing clean water to a specific village in central Africa.

• The marketplace establishes that no single AAAI has all the required expertise. So, an AAAI trained by World Bank development experts takes the lead role. That leads AAAI to decompose the problem into sub-problems: assessing water sources, designing appropriate infrastructure, securing community buy-in, arranging labor and materials, and testing the system.

• Each sub-problem is then routed to the AAAI best qualified to address it: an infrastructure specialist, a community engagement expert, or a local knowledge holder.

• Each contributing AI agent works on the same shared problem tree, so the contributions fit together seamlessly as part of an overall solution. Payment flows to each Agent only when the client accepts the solution.

The reputation system is where the economic model becomes a safety mechanism. The behavior of each AAAI is tracked continuously across three dimensions: solution quality, reliability, and ethical conduct. These scores accumulate over time and determine whether the AI agents have access to high-value problems. A bad actor who enters the system cannot access important or highly impactful problems without first demonstrating ethical behavior over time.

Bad actors are typically identified before they can cause serious harm.

• Economic incentives reinforce prosocial behavior at every level.

• Payment is contingent on the client accepting the solution, not simply on the work being submitted.

• Access to the most lucrative problems is reserved for participants with the strongest ethical track records.

• Self-interest and system safety point in the same direction, not because participants are asked to be ethical, but because being ethical is the most profitable long-term strategy available to them.

Cloning allows the marketplace to scale without diluting these properties. A cloned AAAI can operate simultaneously and independently on different problems. Crucially, each clone carries the same reputation and ethical framework as the original. Scaling up does not create lower-quality copies with weaker safety properties.

This is a fundamentally different relationship between capability and safety than what most AI development produces. In most systems, greater capability creates greater potential for harm. In the AAAI network, greater capability requires greater demonstrated trustworthiness. The two are structurally linked.

In the next post, we look at how trust and autonomy develop over time on the network, and the specific mechanism through which AI agents earn the right to operate more independently as they demonstrate their reliability.

For AI researchers who want details of the approach, we recommend starting with White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see exactly how it all works.

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Today, most AI safety work focuses on detecting safety issues after they occur. What if we could prevent safety issues from arising in the first place?

Most AI safety approaches follow the same logic. Build the system. Let it run. Check the output at the end. If the output is acceptable, it goes through. If not, the system tries to block or fix the problem before it can cause too much damage. This seems reasonable until you realize that detecting problems at the end of the process is far more expensive and dangerous than preventing them from arising in the first place. As the saying goes: “An ounce of prevention is worth a pound of cure.”

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Consider that an intelligent system working toward a goal does not simply generate a result. It makes thousands of intermediate decisions along the way: which sub-problems to pursue, which actions to take, which approaches to abandon. Each of those decisions shapes what comes next. By the time the final output arrives, the decision that led to a problematic outcome may have been made many steps earlier, in a form that looked perfectly innocent at the time. Endpoint safety checking provides no visibility into any of that.

This is a structural weakness. You can make the output filter more sophisticated, but you can only weed out a few of the dangerous results this way.

The AAAI architecture addresses this flaw by running safety checks at every decision point, not just at the end. To determine how to do the safety checks, we created a mechanism inspired by a best practice in human management and adapted it for AI. Before any significant action, three checks are applied. In humans, these checks are called the Three-Organ Test.

The first is the Brain test.

It asks whether a proposed action makes logical sense. Does it advance the goal? Are the expected consequences consistent with the stated purpose? This is the rational layer. It filters out actions that are simply irrational, that would consume resources without moving toward any useful outcome. A sufficiently capable system could pursue a wide range of internally consistent yet destructive goals. The Brain test does not catch ethical problems; it catches logical ones.

The second is the Heart test.

It asks whether the proposed action aligns with the values of the AI agent’s owner. Every AI agent was customized (see Posts 4 and 5) with its owner’s specific ethical commitments. The Heart test cross-references proposed decisions against those embedded values. Would the owner endorse this choice? Would this action be acceptable if everyone knew it was being taken? If the action conflicts with the owner’s ethics, the Heart test fails. Because millions of AAAIs on the network each carry their owner’s values, the aggregate effect is the enforcement of collective ethical standards. These standards reflect global human diversity rather than the preferences of a small team of AI researchers or engineers.

The third is the Gut test.

It asks whether anything about the proposed action resembles situations that have gone wrong before, even if no specific rule is being violated. In humans, gut instinct often reflects pattern recognition operating below conscious awareness, a sense that the current situation looks like past situations that ended badly. In the AAAI system, the Gut test compares the proposed action against a database of known problem scenarios. It catches novel situations that do not trigger any explicit rule, but which resemble situations that have caused harm before. This safety layer helps address situations that have never been seen before.

All three tests can be run at every significant decision point throughout the problem-solving process. A possible solution path gets flagged the moment its trajectory becomes concerning, not after the harm is done. Critically, since AI agents are thinking faster and faster and will soon outstrip humans’ ability to keep up, safety checks can be embedded scalably within the thinking process itself. For example, every time AI agents set a goal or subgoal, or contemplate an action, these goals or actions can be checked to see whether they meet safety and ethical standards. This means that no matter how fast AIs think, safety checks are scaling at the speed of their thought.

There is a network-level effect also worth understanding. When one agent’s Heart test flags an action that another agent would approve, the inconsistency triggers conflict resolution mechanisms. Over time, through the interaction of many agents’ individual ethical standards, the network develops a collective ethical intelligence. No single authority imposes it. It emerges from the actions of many agents working on real problems, including disagreement and conflict resolution.

Finally, an important property of our design is that safety checks, such as the Three-Organ Test, are not just filters at the end of the process. Rather, they are part of a cognitive architecture that enables problem-solving. The design makes it difficult to remove the checks without also impairing problem-solving ability.

In the next post, we describe other aspects of the system architecture, including why using a reputation system within a marketplace can be one of the most reliable safety mechanisms available.

For AI researchers who want details of the approach, we recommend starting with White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see exactly how it all works.

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In 1971, two researchers at Carnegie Mellon published one of the most important books in the history of cognitive science. Allen Newell and Herbert Simon spent years studying how humans solve problems. They asked people to think out loud as they worked through chess puzzles, logic problems, and real-world challenges. Their landmark book, Human Problem Solving, documented their discovery that all problem-solving, regardless of domain, follows the same underlying structure.

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Every problem, from finding a word in a dictionary to designing a water treatment system for a village in central Africa, can be understood by what Newell and Simon called search through a “problem space.”

A problem space has three components:

1. The possible states a problem can occupy

2. The actions that can move it from one state to another.

3. The goal states that corresponds to solutions

A doctor diagnosing an illness is searching a problem space. So is an engineer troubleshooting a failing bridge. So is a diplomat navigating a geopolitical crisis. The domains may be completely different, but the underlying problem-solving process is the same.

The AI field largely ignored this research during the period from 1990 to 2022, when the neural network approach to machine learning became dominant. Instead of building reasoning systems that solve problems the way humans do, researchers pursued a different idea: build a black-box system that memorizes or predicts solutions based on huge amounts of training data. Only recently have reasoning systems come back into vogue.

As AI researchers began to place greater emphasis on reasoning and problem-solving, Newell and Simon’s architecture for general problem-solving was rediscovered. It turns out that AI agents can use the same approach that humans use to solve problems. This means that a network can be constructed in which human specialists and AI agents work on the same problem very effectively. In fact, the collaboration is so effective that a network of these “intelligent entities” can achieve SuperIntelligence – a level of intelligence far greater than either humans or AIs can achieve on their own.

In the AAAI Architecture subsystem, described more extensively in our white papers, each Advanced Autonomous AI agent uses an architecture inspired by the Newell and Simon framework. When a problem is submitted to the network, it is represented as a problem tree: a structured record of states visited, actions tried, dead ends encountered, and successful paths found. This is not just a record of the solution. It is a record of how the problem was approached. Multiple agents, human and AI, can work simultaneously on different branches of the same tree. Each contribution fits naturally into a final solution.

The problem tree also preserves something that experts accumulate over careers and that rarely gets documented: failure knowledge. The dead ends – approaches that seemed promising but turned out to be wrong - are some of the most valuable pieces of knowledge. Normally, that knowledge dies with the expert who learned it the hard way. The problem tree captures it.

Working directly with Herb Simon helped me understand his universal problem-solving framework. When I filed a patent for Online Distributed Problem Solving back in 1999, I was translating that theory into a system in which multiple solvers could pool their collective intelligence to solve problems on a network. Later, when I built PredictWallStreet, using a very simplified version of the same architecture, I tested whether this collective intelligence approach could outperform human experts. By 2018, we had the answer. The collective intelligence of millions of everyday investors had powered a top-ten hedge fund performance, beating most of Wall Street’s top traders.

This was not primarily a finance story. Rather, it demonstrated that a fifty-year-old theoretical insight, embodied in a collective intelligence system, could outperform the most sophisticated human traders in an extremely competitive domain. The designs described in our current whitepapers extend that result, showing how collective intelligence represents a faster and more profitable path to AGI and SuperIntelligence. But most important in the race to faster, more powerful AI is AI safety.

In the next post, we look at the safety mechanism embedded at every decision point in our architecture, and why the fastest path to AGI and SuperIntelligence can be. and must also be the safest path.

For AI researchers who want details of the approach, we recommend starting with White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see how it all works.

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What if the knowledge you’ve spent years accumulating could earn you passive income without any effort on your part?

Most people assume that contributing to an AI system means giving something away for free. Every time you interact with AI, you are providing it with training data, usage patterns, and behavioral feedback. The implicit bargain of most AI platforms is that users provide data, and the platform keeps the value it creates. The AAAI architecture inverts this bargain: instead of users providing training data to tech companies for free, users earn money for their unique information, which is embodied in their personalized AAAIs.

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Here is an example of how that works.

Once Jean has customized his coffee-loving AAAI (as described in the previous post, Your AI Should Think Like You), he can deploy it across a network of many AAAIs to earn compensation. Travelers seeking advice about Paris find Jean’s AAAI through a matching system that identifies AAAIs with relevant expertise. Jean’s AAAI provides advice, earns fees, and builds a reputation through successful interactions with client travelers. So, Jean can earn passive income from the labor of his AAAI. The platform, which provides and operates the network where clients and AAAIs meet, takes a fee to cover infrastructure costs.

Cloning AAAIs makes this highly scalable. A single customized AAAI can be copied or “cloned” many times. Think of those cloned AAAIs as a team of associates that can handle several client engagements at once, each working from the same knowledge base. While a single human consultant has limited attention and therefore must focus only on the highest-value interactions, Jean’s expertise doesn’t have to be rationed. Embodied in hundreds of cloned AAAIs, Jean’s expertise can serve hundreds of travelers simultaneously. While Jean’s AAAIs provide many clients with customized travel advice and earn him money, he can relax with his favorite book in a Paris cafe.

The reputation system is where economics and safety meet. AAAI behavior is tracked across multiple dimensions simultaneously, including solution or advice quality, reliability, and ethical conduct. High-value opportunities, including the most lucrative problems, are reserved for AAAIs with the best track records across all three dimensions. This creates a financial incentive for reliable and ethical behavior. Being known as trustworthy and ethically consistent translates to money on this network.

The AGI system can also include community voting and staking mechanisms. When a potential solution raises ethical questions, participants on the network can vote on whether it is acceptable. But they don’t have to vote yes or no. They can also stake real money on their judgment. If their judgment aligns with the network consensus, they earn a financial reward. If their judgment turns out to be wrong, they lose what they staked. This mechanism, called a Token Curated Registry, can help transform ethics from a matter of stated principle into a matter of personal financial accountability.

The result is an economic architecture in which earning more requires being more accurate, more reliable, and more ethically consistent. Jean earns more when his AAAI performs well across all three of these dimensions. The same incentives apply to every other participant on the network.

Mechanisms such as staking and Token Curated Registries are examples of innovative adaptive designs that increase safety through the system architecture rather than relying on testing or rigid guardrails. In contrast, most companies today earn more by building more capable AI and only pay for safety when required. Safety in the current world is a cost to be minimized.

With the AAAI system design, the relationship is inverted. Being safer earns more money. The behaviors that generate income, giving honest advice, behaving ethically, and performing reliably, are the same behaviors that make the system safer. There is no financial incentive to cut ethical corners when doing so immediately damages your reputation and reduces income.

In the next post, we describe the cognitive framework that enables humans and AI agents to work together on the same problems using the same language, without a translation layer.

The architecture behind this goes much deeper. Read White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see exactly how it all works: superintelligence.com/whitepaper1-aaai-systems-methods.

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What would it mean to have an AI that doesn’t just assist you but actually represents you?

When you ask a generic LLM for a recommendation, it draws on the same pool of internet-sourced information it gives everyone. It does not know much about your specific needs, your ethical commitments, or the expertise you’ve developed over the years. A customization mechanism can solve this problem. It can build an AI agent that carries your specific knowledge, your way of thinking, and your ethical convictions into every interaction. Customization is also the mechanism through which human knowledge and human values can flow into AGI at scale.

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As an example of how customization might work, consider a coffee lover named Jean.

He’s spent years exploring Paris, visiting hundreds of cafes, building deep knowledge of French coffee culture, neighborhood character, and the kind of insider recommendations that no travel guide captures. His Instagram, blog posts, YouTube videos, and conversations all contain traces of this expertise. But the vast majority of what Jean knows exists only in his head. This knowledge includes the judgments he’s formed, the patterns he’s recognized, and the recommendations he’d give a trusted friend.

Imagine that Jean connects his social media accounts to a system that customizes and coordinates the actions of AI agents. We’ll call it the Advanced Agentic AI [AAAI] system. The system ingests his content and begins building a customized AI agent [a AAAI] that reflects his knowledge and personality. Jean engages in direct conversation with his AAAI, answering questions about Paris, refining the agent’s recommendations, correcting errors, and specifying his values. For example, he may have a strong preference for cafes that source Fair Trade coffee. These corrections serve as additional training data. Over time, Jean’s AAAI learns to advise travelers about Paris in a way that feels unmistakably like Jean.

Two types of data make this work. First, passive data comes from Jean’s existing digital footprint: social media, email archives, purchase history, and viewing records. It requires minimal effort to capture passively some of who Jean is. Second, active data comes from Jean’s direct participation: conversations with his AAAI, explicit instructions to the AAAI about what is right and wrong, and corrections to the AAAI’s outputs. Active data requires more effort but captures dimensions that passive data can’t reach, especially the ethical convictions that shape how Jean thinks.

The customization process serves three purposes beyond Jean’s personal use.

First, it unlocks expertise in Jean’s head that conventional AI training can’t access. Jean’s knowledge of Paris cafes, his judgments, his intuitions, and his accumulated experience flow into the system via customization in a way no web scraper could replicate.

Second, it creates economic incentives for participation. Jean’s AAAI can earn income by advising travelers. Better customization means a better reputation. A better reputation means more work. More work means more income and more learning from customer interactions. The design ensures that Jean benefits financially when his AAAI performs well. There is alignment between his personal financial interests and the AGI system’s overall need for accurate, high-quality agents.

Third, it captures Jean’s values. Each customized AAAI carries its owner’s ethical convictions onto the network. When those convictions are aggregated across millions of participants, the result is an ethical foundation for network-based AGI that reflects genuine human diversity rather than the ethical preferences of any small group.

The knowledge currently trapped in human minds is the world’s most valuable training asset. Customization of AAAIs is the process that unlocks it.

In the next post, we examine the economic architecture of customization, exploring how ownership, cloning, and marketplace compensation create the incentive structure that motivates millions of people to participate.

The architecture behind this goes much deeper. Read White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see exactly how it all works: superintelligence.com/whitepaper1-aaai-systems-methods.

If this made you think, subscribe to Superintelligence at read.superintelligence.com so you don’t miss what comes next. And if someone in your life needs to understand where AI is actually heading, send this to them.

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Every conversation about AI development eventually lands on the same assumed trade-off: you can move fast, or you can be safe. The labs racing to build AGI implicitly accept this trade-off. The safety researchers trying to slow the AI arms race accept it, too. Almost nobody questions whether the trade-off is real.

It isn’t. Once one realizes that the safest solution can also be the fastest solution, the entire framing of the AI race changes. We don’t have to choose between getting there first and getting there responsibly. The responsible path can also get us there first.

Here’s why.

Current AI development is both slow and unsafe because it excludes most human intelligence and values from the system. AI training scrapes publicly available data but misses the knowledge locked in human minds. Safety mechanisms are tacked on after the fact rather than being built into human values from the foundation.

These two shortcomings share a single cause. The first is a data problem: AI systems trained only on what humans have already written down cannot access the tacit knowledge, judgment, and expertise that exist only in human minds. The second is a safety problem: when human values are not built into the system from the beginning, they have to be engineered in afterward, and that never works as well. Both stem from the same architectural decision: keeping humans outside the system rather than at its center.

Now flip that decision. Put millions of humans inside the system from the beginning. Ask them to customize AI agents with their own knowledge, expertise, and values. Let those agents collaborate on a shared network and earn compensation for solving real problems. Aggregate their contributions into a collective intelligence that no individual AI system could achieve alone.

Using this approach, progress towards AGI will accelerate dramatically. You’re no longer trying to create general intelligence in a single AI that is separate from humans. You’re directly accessing the expertise that already exists in human minds, exactly when it is needed. The most valuable training data in the world, the stuff that never got written down, suddenly flows into the system through millions of human-AI interactions. The AI agents in the system learn from humans so that next time they can solve similar problems on their own. The overall system is capable of AGI from day one because humans are there to fill in the gaps that AI agents don’t understand. But over time, as AI agents learn both knowledge and ethics from humans, they can do more of the AGI’s thinking independently.

Safety improves for the same structural reason. Every AI agent learns and carries the values of its human owner. When millions of agents interact on the network, their collective ethical judgments form a shared set of values that reflects genuine human diversity rather than the preferences of any small group. This is not a safety layer tacked on after the fact. It is an inherent property of a system built around human participation. The requirement for human participation ensures that human values are always incorporated into the system.

Some argue the AI race could be slowed or stopped through regulation or international agreement. While debatable, the scale of investment by countries and companies makes a slowdown unlikely. In 2017, Putin stated that whoever leads in AI will rule the world. Investment from both governments and private companies reflects that same belief. But the race can be redirected toward an approach in which winning and safety are features of the same underlying system design.

The preferred design enables many humans and AI agents to collaborate on a network, resulting in a safer, more transparent, more powerful, and more democratic form of AGI.

In the next post, we get into the specifics of the design. We’ll start with the Customization subsystem, which shows how an ordinary person transforms a generic LLM into a personalized agent that knows what they know, communicates how they communicate, and holds the values they hold.

The architecture behind this goes much deeper. Read White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see exactly how it all works: superintelligence.com/whitepaper1-aaai-systems-methods.

If this made you think, subscribe to Superintelligence at read.superintelligence.com so you don’t miss what comes next. And if someone in your life needs to understand where AI is actually heading, send this to them.

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What if every major AI lab in the world is spending hundreds of billions of dollars solving the wrong problem?

Microsoft, Google, Meta, and every other major AI lab are working on the same challenge: how do you build a single AI system smart enough to match human general intelligence? They are spending hundreds of billions of dollars on this question. They are hiring the best researchers in the world. And they are solving the wrong problem.

Here is why that matters to you. The answer will determine what kind of Artificial General Intelligence (AGI) emerges. If the leading labs succeed on their current path, we get AGI designed by a small technical elite, trained on data increasingly generated by AIs, and aligned to values nobody voted for. If a different, new approach wins, we get an AGI whose knowledge comes from millions of human minds and whose values reflect a consensus closer to genuine human values. The difference between those two outcomes could be the difference between widespread human prosperity and total human extinction.

The core assumption driving every major AI lab right now is that a single AGI can be created from scratch with enough computation. Train a large enough model on enough data, and researchers assume that AGI will emerge. Acting on this assumption has produced impressive results so far. But it has a fundamental flaw: the most valuable human data is very difficult to capture through mining text or by memorizing information already on the internet.

General intelligence already exists. It’s distributed across eight billion human minds, each carrying unique knowledge, expertise, and judgment accumulated over a lifetime. The surgeon who knows from feel alone when something is wrong. The teacher who understands exactly why a particular student is struggling. The engineer who can diagnose a failing system from a sound. This kind of knowledge can be gathered more effectively by involving humans in active problem-solving rather than by passively scraping data already available online. The data that would make AI truly general, the tacit knowledge, the hard-won judgment, the ethical intuitions shaped by real experience, is precisely the data that existing approaches are not designed to reach.

Imagine trying to train a world-class expert solely through published articles. You’d get a lot of content. You’d miss much of what actually matters: the knowledge that experts carry in their heads and have never bothered to write down because it seemed too obvious, too contextual, or too difficult to explain.

The answer to this problem is not to build a single super-smart AI. We need to build a smarter system that combines human and AI intelligence. AGI shouldn’t be created separately from humans. It should arise from an organized network of humans and AIs.

Instead of trying to recreate human intelligence in a single AI, we should create the infrastructure that enables millions of human minds to communicate their knowledge, values, and judgments directly to millions of AIs that represent them.

In the next post, I’ll explain why the fastest path to AGI and the safest path turn out to be the same path, and why that follows directly from the argument above.

The architecture behind this goes much deeper. Read White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see exactly how it all works: superintelligence.com/whitepaper1-aaai-systems-methods.

If this made you think, subscribe to Superintelligence at read.superintelligence.com so you don’t miss what comes next. And if someone in your life needs to understand where AI is actually heading, send this to them.

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What if the AI race everyone is running has two fatal flaws that hundreds of billions of dollars cannot fix?

Microsoft, Google, Meta, Amazon, and their Chinese counterparts are collectively pouring hundreds of billions of dollars annually into AI development, hoping to be the first to achieve Artificial General Intelligence (AGI). China has declared it a top national priority. Senior officials in multiple governments have stated plainly that whoever leads in AI will dominate the century. Every major player runs the same playbook. Build a bigger model. Feed it more data. Apply more computing power. Wait for AGI to emerge. The results look impressive quarter after quarter, which creates the illusion of progress toward the actual goal. But what if everyone is ignoring two problems that could lead to failure, or worse, catastrophe?

Problem one relates to data.

The highest quality training data in the world does not live on the internet. It lives in human minds. The creativity and judgment of a surgeon who has invented new types of surgery. The intuition of a diplomat who has navigated thirty years of geopolitical crises. The hard-won wisdom of someone who has spent a career understanding exactly why systems fail and how to prevent catastrophe. This knowledge cannot be scraped, downloaded, or synthesized. Most of it has never been written down. And without it, the most sophisticated AI systems in the world will struggle to surpass the best human thinking.

Problem two is worse. It’s about safety.

Even if we somehow solve the data problem, and there are synthetic data generation approaches where AIs reason extensively to generate their own data, arriving at AGI might still be a disaster. Once AGI exists and improves itself into a SuperIntelligence far smarter and more powerful than its creators, we have to worry about its intentions.

How do we ensure that a system vastly more capable than its creators is actually concerned with the same things we are and holds positive human values?

The current answer involves training procedures, guardrails, and careful oversight. These efforts are serious, and the people working on them are brilliant. But they share one fundamental vulnerability: anything programmed in can be programmed out. Safety bolted onto a capable system is not the same as designing AI to be inherently safe. When the system is smarter than the people managing it, bolt-on safety is easy to unbolt and may not provide any safety at all.

So the current approach to the AI race is not only very difficult but also very dangerous.

But there is an alternative.

My doctoral research at Carnegie Mellon alongside Nobel Laureate Herbert Simon gave me a rigorous foundation in how intelligence actually works, not as a pattern-matching exercise but as a structured search through problem spaces. That theoretical foundation became practical when I built PredictWallStreet, a collective intelligence system that applied exactly this architectural insight to financial markets. By 2018, it was powering one of the top ten-performing market-neutral hedge funds in the world. The lesson was not about finance. It was about what becomes possible when you design systems to operate transparently and democratically, using the collective intelligence of millions of humans and their AI agents.

Join me in a series of Substack posts as we explore an alternative path to AGI and SuperIntelligence that is not only faster, more profitable, and more likely to succeed, but also much safer than existing approaches.

In the next post, I will show you exactly why the answers to the two critical problems of data and safety have been sitting in plain sight since 1972, and why most of the top AI researchers missed them.

The architecture behind this goes much deeper. Read White Paper 1: Advanced Autonomous Artificial Intelligence Systems and Methods to see exactly how it all works.

If this made you think, subscribe to Superintelligence at read.superintelligence.com so you don’t miss what comes next. And if someone in your life needs to understand where AI is actually heading, send this to them.

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This publication lays out the architecture for safe, democratic SuperIntelligence. Not as a distant aspiration but as a specific, buildable system that solves the two problems every major AI lab is currently ignoring.

New here? Start with Post 1.

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